Water vapor retrieval using the FLAASH atmospheric correction algorithm

Gerald W. Felde; Gail P. Anderson; James A. Gardner; Steven M. Adler-Golden; Michael W. Matthew; Alexander Berk

doi:10.1117/12.542513

12 August 2004 Water vapor retrieval using the FLAASH atmospheric correction algorithm

Gerald W. Felde, Gail P. Anderson, James A. Gardner, Steven M. Adler-Golden, Michael W. Matthew, Alexander Berk

Proceedings Volume 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X; (2004) https://doi.org/10.1117/12.542513
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) is a first-principles atmospheric correction algorithm for visible to shortwave infrared (SWIR) hyperspectral data. The algorithm consists of two main steps. The first is retrieval of atmospheric parameters, visibility (which is related to the aerosol type and distribution) and column water vapor. The second step is solving the radiation transport equation for the given aerosol and column water and transformation to surface reflectance. The focus of this paper is on the FLAASH water vapor retrieval algorithm. Modeled radiance values in the spectral region of one water vapor absorption feature are calculated from MODTRAN 4 using several different water vapor amounts and are used to generate a Look-Up Table (LUT). The water band typically used is 1130 nm but either the 940 or 820 nm band may also be used. Measured radiance values are compared to the LUT to determine the column water vapor amount for each pixel in the scene. We compare the results of water retrievals for each of these bands and also the results of their corresponding reflectance retrievals.

Citation Download Citation

Gerald W. Felde, Gail P. Anderson, James A. Gardner, Steven M. Adler-Golden, Michael W. Matthew, and Alexander Berk "Water vapor retrieval using the FLAASH atmospheric correction algorithm", Proc. SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, (12 August 2004); https://doi.org/10.1117/12.542513

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